Recent progress in artificial intelligence has reshaped the design philosophy of modern operating systems, with Large Language Models (LLMs) emerging as a key enabling technology. Existing operating systems primarily rely on predefined graphical and command-line interactions, which limits their ability to adapt to user preferences, understand context, and respond to complex intent. Early natural language–based interfaces attempted to address these limitations; however, they lacked robust contextual reasoning and system-level intelligence required for dynamic computing environments. As workloads and user expectations continue to grow in complexity, there is an increasing need for operating systems capable of understanding and acting upon natural language instructions in a reliable and transparent manner. This paper examines operating system architectures that embed LLMs within core system components to facilitate intent-driven interaction and intelligent automation. The study surveys representative frameworks such as AIOS, PEROS, Compressor–Retriever architectures, and Herding LLaMaS, emphasizing how these designs translate linguistic input into executable operating system functions. Key capabilities explored include continuous context retention, dynamic allocation of computational resources, and semantic coordination between software services and underlying hardware. These design choices aim to enhance usability while reducing the cognitive burden placed on end users. The discussion further addresses essential system considerations, including interpretability of model-driven decisions, user-specific adaptation, protection of sensitive data, and scalability across diverse computing platforms. Emphasis is placed on explainable reasoning mechanisms that help users and developers understand system behavior, particularly in safety- and security-critical scenarios. By integrating adaptable intelligence with transparent control logic, LLMenhanced operating systems can improve trust and operational efficiency.
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A Comprehensive Literature Survey on Large Language Model-Driven Operating Systems
Semantic Scholar · 2025
Abstract
Recent progress in artificial intelligence has reshaped the design philosophy of modern operating systems, with Large Language Models (LLMs) emerging as a key enabling technology. Existing operating systems primarily rely on predefined graphical and command-line interactions, which limits their ability to adapt to user preferences, understand context, and respond to complex intent. Early natural language–based interfaces attempted to address these limitations; however, they lacked robust contextual reasoning and system-level intelligence required for dynamic computing environments. As workloads and user expectations continue to grow in complexity, there is an increasing need for operating systems capable of understanding and acting upon natural language instructions in a reliable and transparent manner. This paper examines operating system architectures that embed LLMs within core system components to facilitate intent-driven interaction and intelligent automation. The study surveys representative frameworks such as AIOS, PEROS, Compressor–Retriever architectures, and Herding LLaMaS, emphasizing how these designs translate linguistic input into executable operating system functions. Key capabilities explored include continuous context retention, dynamic allocation of computational resources, and semantic coordination between software services and underlying hardware. These design choices aim to enhance usability while reducing the cognitive burden placed on end users. The discussion further addresses essential system considerations, including interpretability of model-driven decisions, user-specific adaptation, protection of sensitive data, and scalability across diverse computing platforms. Emphasis is placed on explainable reasoning mechanisms that help users and developers understand system behavior, particularly in safety- and security-critical scenarios. By integrating adaptable intelligence with transparent control logic, LLMenhanced operating systems can improve trust and operational efficiency.